In the realm of class action litigation, the duty to safeguard class members is a foundational principle. Yet, discussions around fraud prevention often shift the focus toward the cost of implementation rather than its impact. While fiscal prudence is warranted, centering the conversation solely on expense misses the more urgent and consequential inquiry: What is the value of an effective fraud detection system, and what are the implications of neglecting it?
Fraudulent claims are not a theoretical concern. When fraud prevention is inadequate, the result is a direct and substantial erosion of the settlement’s value. These claims dilute the compensation available to legitimate class members, distort equitable outcomes, and compromise trust in the class action mechanism; if left unchecked, fraud becomes a structural vulnerability that jeopardizes the integrity of the entire process.
To contextualize the scale of the issue, here’s an example of one (of many) cases that we were recently retained in to analyze claim data. It was the settlement of a large scale consumer class action involving the sale of gem stones. Plaintiffs’ counsel was, legitimately, concerned that fraud would reduce the payout to each legitimate class member, and asked us to work with the settlement administrator to ensure that fraud was completely eliminated from the settlement, and that legitimate class members obtained the maximum payout from the $5,750,000 common fund. The administrator, ultimately, received 2,442,606 claims, of which the administrator preliminarily approved 92,890. Under that preliminary analysis, $61.90 was to be paid to each class member.
Our analysis, however, determined that 2,422,065 claims were actually fraudulent, and that only 20,541 were made by legitimate claimants. Under that analysis, which was ultimately adopted by the administrator, $279.93 was paid to each class member. Said differently, a full and complete fraud analysis resulted in an increase of 350% to each class member, and a savings of $4,478,403.10 which otherwise would have been paid to fraudsters. In sum, without that full analysis, almost 80% of the fund would have been paid to fraudsters. A striking ROI for the class.
It should be noted that the effect of this improvement depends on the structure of the settlement. In a common fund structure, like the real case referenced above, these same improvements mean that valid claimants receive potentially over 4.5x times more than they would have in a fraud-heavy distribution. In a claims-made arrangement, the fraudulent claims prevented from being paid, directly reduce the total disbursement, potentially by as much as 80%.
This isn’t a marginal operational upgrade. It is a recalibration of fairness, restoring value to those who are entitled to it. Redirecting funds toward robust fraud prevention is not an increase in overhead; it is a deliberate, strategic reinvestment in the class’s actual recovery. Every dollar spent on quality fraud detection effectively removes fraudulent claims from the payout pool and reroutes those dollars back to legitimate class members.
Viewed this way, the cost of the fraud solution, whether $10,000, $50,000, or $100,000, becomes secondary to the magnitude of the impact it generates. When fraud prevention yields tens of millions in preserved or returned funds, the logic of cost aversion becomes harder to justify. Prioritizing sticker price over performance ignores the fiduciary responsibility to secure the greatest possible outcome for the class.
As we’ve noted throughout this series, fraud prevention is best judged not by how little it costs, but by how much it protects. The efficacy of a system should be measured in terms of accuracy, scalability, and its demonstrable ability to safeguard settlement. So the question isn’t whether your fraud prevention system is cheap—it’s whether it’s working. Because the real cost isn’t the price of the tool. It’s what you lose without it.
Learn how ClaimScore helps class action stakeholders protect their settlements with fraud detection that's accurate, scalable, and proven to return money to the class.
